HealthcareLCA: an open-access living database of health-care environmental impact assessments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Anthropogenic environmental change negatively effects human health and is increasing health-care system demand. Paradoxically, the provision of health care, which itself is a substantial contributor to environmental degradation, is compounding this problem. There is increasing willingness to transition towards sustainable health-care systems globally and ensuring that strategy and action are informed by best available evidence is imperative. In this Personal View, we present an interactive, open-access database designed to support this effort. Functioning as a living repository of environmental impact assessments within health care, the HealthcareLCA database collates 152 studies, predominantly peer-reviewed journal articles, into one centralised and publicly accessible location, providing impact estimates (currently totalling 3671 numerical values) across 1288 health-care products and processes. The database brings together research generated over the past two decades and indicates exponential field growth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it